What are dynamic optimization problems?

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Abstract

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.

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  • Dynamic optimization problems

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Details

Publication date2014
Publication titleProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherIEEE
Pages1550-1557
Number of pages8
ISBN (Print)978-1-4799-6626-4
Original languageEnglish
Event2014 IEEE Congress on Evolutionary Computation - Beijing, China

Congress

Congress2014 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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